CN112153573A - Segmentation method and device based on position track, computer equipment and storage medium - Google Patents

Segmentation method and device based on position track, computer equipment and storage medium Download PDF

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CN112153573A
CN112153573A CN202011039063.XA CN202011039063A CN112153573A CN 112153573 A CN112153573 A CN 112153573A CN 202011039063 A CN202011039063 A CN 202011039063A CN 112153573 A CN112153573 A CN 112153573A
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reported data
preset
interval
condition
position information
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CN112153573B (en
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陈向键
李平
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Ping An International Financial Leasing Co Ltd
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Ping An International Financial Leasing Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/024—Guidance services
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/025—Services making use of location information using location based information parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H04W4/48—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for in-vehicle communication
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Traffic Control Systems (AREA)
  • Navigation (AREA)

Abstract

The invention discloses a segmentation method and device based on position tracks, computer equipment and a storage medium, and belongs to the field of big data analysis. The position track-based segmentation method judges whether the reported data belong to the travel point or not by identifying whether the reported data meet the preset conditions or not according to the preset interval, and extracts abnormal points; when the reported data belong to the abnormal point, whether the adjustment interval condition is met is judged according to the position information of the reported data, if yes, the current moving body is in the area with weak signals, the preset interval needs to be prolonged, so that the condition that the stroke segmentation is wrong due to the fact that the signals of the area where the moving body is located are weak and the reported data are delayed is avoided, and the accuracy of the stroke segmentation is improved.

Description

Segmentation method and device based on position track, computer equipment and storage medium
Technical Field
The invention relates to the field of big data analysis, in particular to a segmentation method and device based on position tracks, computer equipment and a storage medium.
Background
At present, track segmentation modes are mainly divided into two categories, one is a track and stroke segmentation mode based on vehicle power-on and flameout signals; and the other type is a trip segmentation mode based on the reported information of the GPS system.
The track segmentation method based on the vehicle power-on and flameout signals has the advantages of being accurate in stroke segmentation and good in timeliness. However, the track segmentation mode based on the vehicle power-on and flameout signals needs to be installed in a broken line mode, when the vehicle power-on and flameout signals are acquired in an error mode, the track cannot be segmented, the error can be repaired only in a rewiring mode, and the reinstallation cost is very high.
Compared with a track segmentation mode which needs to be installed in a broken line mode and is based on vehicle power-on and flameout signals, the mode which is convenient to install and maintain and adopts GPS data to conduct driving segmentation is favored by people. However, in the current driving segmentation mode based on the GPS data, traffic jam road sections, cave road sections, and tunnel road sections are not considered, and data reporting is affected due to no signal or weak signal, so that data reporting is delayed, thereby affecting driving trajectory segmentation, and errors occur.
In summary, the existing driving switching method based on GPS data does not consider the influence of weak signal or no signal area on data reporting.
Disclosure of Invention
Aiming at the problem that the influence of a weak signal or no signal area on data reporting is not considered in the conventional driving cutting method based on GPS data, a cutting method, a device, computer equipment and a storage medium based on a position track are provided, which aim at adjusting an acquisition time interval based on position information of reported data so as to improve the track cutting accuracy.
In order to achieve the above object, the present invention provides a segmentation method based on position trajectory, comprising:
acquiring reported data of a mobile body;
identifying whether the reported data meets preset conditions or not according to preset intervals;
if the reported data does not accord with the preset condition, judging whether the position information of the reported data accords with the condition of an adjustment interval or not;
if the position information of the reported data accords with the adjustment interval condition, adjusting the preset interval according to the reported data and historical data associated with the moving body;
and if the position information of the reported data does not accord with the adjustment interval condition, taking the reported data as a starting dividing point of the current trip, and taking the last reported data of the historical data associated with the moving body as an ending dividing point of the last trip.
Optionally, identifying whether the reported data meets a preset condition according to a preset interval, where the method further includes:
and identifying whether the reported data is data carrying position information, if so, identifying whether the reported data meets a preset condition according to the preset interval.
Optionally, identifying whether the reported data meets a preset condition according to a preset interval includes:
calculating a time interval between the reported data and the last reported data according to the reported data and the historical data associated with the mobile body;
judging whether the time interval meets a preset condition or not, wherein the preset condition is as follows: the time interval is less than or equal to the preset interval.
Optionally, the adjustment interval condition is that the position information in the reported data is in a first preset range;
the first preset range is at least one of a low signal area, a speed limit area and a congestion area.
Optionally, if the position information of the reported data meets the adjustment interval condition, adjusting the preset interval according to the reported data and the historical data associated with the mobile body, including:
if the reported data accords with the adjustment interval condition, acquiring standard driving time of an area corresponding to the position information in the reported data;
and summing the standard running time and the preset interval, and taking the summed value as the preset interval.
Optionally, if the position information of the reported data meets the adjustment interval condition, adjusting the preset interval according to the reported data and the historical data associated with the mobile body, including:
if the reported data accords with the adjustment interval condition, acquiring standard running time of a region corresponding to the position information in the reported data and current running average time of the region corresponding to the position information;
summing the standard running time and the preset interval to obtain a sum value; acquiring the average value of the summation value and the current running average time, and taking the average value as the preset interval; or the like, or, alternatively,
and comparing the summation value with the current running average value, and taking the larger value in the comparison result as the preset interval.
Optionally, if the position information of the reported data does not meet the adjustment interval condition, taking the reported data as a starting cut point of a current trip, and taking last reported data of the history data associated with the mobile body as an ending cut point of a last trip, including:
and if the position information of the reported data does not accord with the adjustment interval condition, identifying whether the position information of the reported data is in a second preset area, and if not, taking the reported data as a starting cut point of the current trip and taking the last reported data of the historical data associated with the moving body as an ending cut point of the last trip.
In order to achieve the above object, the present invention further provides a segmentation apparatus based on position trajectory, including:
the mobile terminal comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring the reported data of the mobile body;
the identification unit is used for identifying whether the reported data meets a preset condition or not according to a preset interval;
a judging unit, configured to judge whether the reported data meets an adjustment interval condition when the location information of the reported data does not meet a preset condition;
an adjusting unit, configured to adjust the preset interval according to the reported data and historical data associated with the mobile body when the position information of the reported data meets the adjustment interval condition;
and a segmentation unit, configured to, when the position information of the reported data does not meet the adjustment interval condition, use the reported data as a starting segmentation point of a current trip, and use last reported data of the history data associated with the mobile body as an ending segmentation point of a last trip.
To achieve the above object, the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the steps of the above method when executing the computer program.
To achieve the above object, the present invention also provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the above method.
According to the segmentation method, the segmentation device, the computer equipment and the storage medium based on the position track, whether the reported data belong to the travel point or not is judged in a mode of identifying whether the reported data meet the preset conditions or not according to the preset intervals, and the abnormal point is extracted; when the reported data belong to the abnormal points, whether the adjustment interval condition is met is judged according to the position information of the reported data, if so, the current moving body is in the area with weak signals (such as a tunnel road section, a congestion road section and the like), the preset interval needs to be prolonged, so that the condition that the travel segmentation is wrong due to the fact that the reported data are delayed because the area where the moving body is located is weak in signals is avoided, and the accuracy of the travel segmentation is improved.
Drawings
FIG. 1 is a flow chart of an embodiment of a segmentation method based on a position trajectory according to the present invention;
fig. 2 is a flowchart illustrating an embodiment of identifying whether reported data meets a predetermined condition according to a predetermined interval;
FIG. 3 is a block diagram of an embodiment of a slicing apparatus based on a position trajectory according to the present invention;
FIG. 4 is a block diagram of an identification unit of the present invention;
FIG. 5 is a block diagram of another embodiment of the apparatus for slicing based on position trajectory according to the present invention;
fig. 6 is a schematic hardware architecture diagram of an embodiment of a computer device according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The segmentation method, the segmentation device, the computer equipment and the storage medium based on the position track can be applied to rental services and intelligent traffic services, and can judge whether the reported data belong to the travel point or not and extract abnormal points by identifying whether the reported data meet preset conditions or not according to preset intervals; when the reported data belong to the abnormal points, whether the adjustment interval condition is met is judged according to the position information of the reported data, if so, the current moving body is in the area with weak signals (such as a tunnel road section, a congestion road section and the like), the preset interval needs to be prolonged, so that the condition that the travel segmentation is wrong due to the fact that the reported data are delayed because the area where the moving body is located is weak in signals is avoided, and the accuracy of the travel segmentation is improved.
Example one
Referring to fig. 1, a segmentation method based on a position trajectory according to the embodiment may include the following steps:
s1, acquiring reported data of a mobile body.
The moving body in the embodiment may be a new energy vehicle such as a pure electric vehicle, a hybrid electric vehicle, or the like, or may be a gasoline vehicle, a fuel vehicle, or the like, and is not particularly limited. The reported data can be any one of GPS data, Beidou (BDS) data or Glonass (GLONASS).
S2, identifying whether the reported data meet preset conditions or not according to preset intervals, and if so, executing a step S1; if not, go to step S3.
Wherein the preset condition is that the time interval is less than or equal to the preset interval (such as 2 minutes, 3 minutes, etc.).
In this embodiment, whether the reported data belongs to the trip point is determined by identifying whether the reported data meets the preset condition according to the preset interval, and if the reported data meets the preset condition, the reported data is the trip point, and the step S1 may be executed; if the reported data is not in accordance with the preset condition, it indicates that the reported data is an abnormal point, and step S3 needs to be executed to further analyze the reported data. The abnormal point is a point which is not suitable for track segmentation, and the position information of the abnormal point needs to be further analyzed.
Specifically, step S2 shown in fig. 2 may include the following steps:
and S21, calculating the time interval between the reported data and the last reported data according to the reported data and the historical data associated with the mobile body.
It is emphasized that, in order to further ensure the privacy and security of the historical data, the historical data may also be stored in a node of a blockchain. The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
And S22, judging whether the time interval meets a preset condition.
Taking the reported data as GPS data as an example, the reported data includes an IMEI code identifying the mobile body, and the historical data associated with the IMEI code can be queried in a database storing the historical data according to the IMEI code, that is: determining the last reported data which is most adjacent to the current reported data according to the time stamp of the data by the historical data of the moving body, subtracting the time stamps of the two reported data to obtain a time interval, judging whether the time interval is less than or equal to a preset interval, if so, indicating that the reported data is a travel point, and recording the reported data into a database; if not, it indicates that the reported data is an abnormal point, and step S3 is executed.
In an embodiment, before performing step S2, the method may further include:
and identifying whether the reported data is data carrying position information, if so, executing step S2.
In this embodiment, in order to reduce the processing pressure of the massive reported data uploaded by the mobile body and improve the accuracy of data processing, data cleaning may be performed on the reported data, and data without location information may be filtered, for example: heartbeat data and alarm data (such as electric quantity alarm data, disassembly alarm data and the like).
S3, judging whether the position information of the reported data meets the adjustment interval condition, if so, executing the step S4; if not, go to step S5.
It should be noted that: the adjustment interval condition is that the position information in the reported data is in a first preset range; the first preset range is at least one of a low signal area, a speed limit area and a congestion area.
In this embodiment, in consideration of the situation that a moving body may approach a low-signal, speed-limited or congested road segment during a driving process, so as to prolong a time interval of reported data, the reported data meeting the above situation is extracted by adjusting an interval condition, so that a preset interval is dynamically adjusted according to the situation of the road segment, and a situation that a path is erroneously split due to a fixed preset interval is prevented.
And S4, if the position information of the reported data meets the adjustment interval condition, adjusting the preset interval according to the reported data and the historical data associated with the moving body, and executing the step S1.
In one embodiment, step S4 may include:
if the reported data accords with the adjustment interval condition, acquiring standard driving time of an area corresponding to the position information in the reported data; and summing the standard running time and the preset interval, and taking the summed value as the preset interval.
In this embodiment, each first preset area corresponds to a standard travel time, the standard travel time is a fixed travel time passing through the first preset area, and a time interval obtained by summing the standard travel time and a preset interval is used as an adjusted preset interval, so as to achieve the purpose of prolonging the preset interval.
For example: the first preset area is a tunnel area, and the standard driving time T of the tunnel area1Tunnel length (tunnel group length)/lowest speed limit, adjusted preset interval T2＝T0+T1Wherein, T0At an initial preset interval.
In another embodiment, step S4 may include:
if the reported data accords with the adjustment interval condition, acquiring the standard running time of a region corresponding to the position information in the reported data and the current running average time of the region corresponding to the position information, summing the standard running time and the preset interval to acquire a summation value, acquiring the average value of the summation value and the current running average time, and taking the average value as the preset interval.
In the present embodiment, the current running average time is the average running time of all moving objects that approach the area corresponding to the position information (the first preset area) within a preset time period (for example, 1 hour or 2 hours). Adjusted preset interval T2＝(T0+T1+T3) /2, wherein, T3Is the current running mean time.
In another embodiment, step S4 may include:
if the reported data accords with the adjustment interval condition, acquiring the standard running time of a region corresponding to the position information in the reported data and the current running average time of the region corresponding to the position information, summing the standard running time and the preset interval to acquire a summation value, comparing the summation value with the current running average value, and taking the larger value in the comparison result as the preset interval.
And S5, if the position information of the reported data does not accord with the adjustment interval condition, taking the reported data as a starting dividing point of the current trip, and taking the last reported data of the historical data associated with the moving body as an ending dividing point of the last trip.
Further, step S5 includes:
and if the position information of the reported data does not accord with the adjustment interval condition, identifying whether the position information of the reported data is in a second preset area, and if not, taking the reported data as a starting cut point of the current trip and taking the last reported data of the historical data associated with the moving body as an ending cut point of the last trip.
The second preset area is a preset highway section and/or a preset elevated road section.
Considering that when the position information of the reported data may be in a certain high-speed road section/elevated road section, it is obvious that the road section is not suitable for track segmentation, and the moving body may be in traffic jam or reporting equipment failure at present, the reported data can be regarded as a travel point and recorded in a database without performing subsequent process processing.
In the embodiment, the problem that the track is segmented into unreasonable areas can be solved. Such as the case of splitting a trajectory path at a highway segment. Of course, the coordinates of the high-speed service area and the like are not taken as abnormal points, so that the track segmentation of the service area is not influenced.
In this embodiment, the position trajectory based segmentation method determines whether the reported data belongs to a trip point by identifying whether the reported data meets a preset condition according to a preset interval, and extracts an abnormal point; when the reported data belong to the abnormal points, whether the adjustment interval condition is met is judged according to the position information of the reported data, if so, the current moving body is in the area with weak signals (such as a tunnel road section, a congestion road section and the like), the preset interval needs to be prolonged, so that the condition that the travel segmentation is wrong due to the fact that the reported data are delayed because the area where the moving body is located is weak in signals is avoided, and the accuracy of the travel segmentation is improved.
In an embodiment, after performing step S5, the method for splitting based on the position trajectory may further include: and initializing the preset interval so as to analyze the reported data with the original preset interval as the reference when the reported data of the moving body is analyzed next time.
In this embodiment, the segmentation method based on the position trajectory can avoid the phenomenon of trajectory miscut caused by areas without signals or weak signals. For example, in the areas such as tunnels, tunnel groups, cross-sea bridges and the like, due to uploading delay caused by weak network signals, trace points and segmentation routes exceeding preset intervals are generated, and the situation can be avoided by dynamically adjusting the preset intervals. For track segmentation of congested road sections, dynamically adjusting a preset interval by current running average time obtained by calculating mass points, and also avoiding the situation; the accuracy rate of track errors of congested road sections generated in urban on-duty peak hours can be improved.
Example two
Referring to fig. 3, a slicing apparatus 1 based on a position track of the present embodiment includes: the device comprises an acquisition unit 11, a recognition unit 12, a judgment unit 13, an adjustment unit 14 and a cutting unit 15.
The acquiring unit 11 is configured to acquire report data of a mobile body.
The moving body in the embodiment may be a new energy vehicle such as a pure electric vehicle, a hybrid electric vehicle, or the like, or may be a gasoline vehicle, a fuel vehicle, or the like, and is not particularly limited. The reported data can be any one of GPS data, Beidou data or Glonass.
And the identifying unit 12 is configured to identify whether the reported data meets a preset condition according to a preset interval.
Wherein the preset conditions are as follows: the time interval is less than or equal to the preset interval.
In this embodiment, whether the reported data belongs to a trip point is determined by identifying whether the reported data meets a preset condition according to a preset interval, and if the reported data meets the preset condition, the reported data is a trip point, and the reported data of the mobile body is acquired by the acquisition unit 11; if the reported data is not in accordance with the preset condition, the reported data is represented as an abnormal point, and the judging unit 13 is adopted to further analyze the reported data. The abnormal point is a point which is not suitable for track segmentation, and the position information of the abnormal point needs to be further analyzed.
Specifically, referring to fig. 4, the recognition unit 12 may include: a calculation module 121 and a judgment module 122.
A calculating module 121, configured to calculate a time interval between the reported data and the last reported data according to the reported data and the historical data associated with the mobile body.
Wherein the historical data may be stored in nodes of a blockchain. The block chain referred by the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The determining module 122 is configured to determine whether the time interval meets a preset condition.
Taking the reported data as GPS data as an example, the reported data includes an IMEI code identifying the mobile body, and the historical data associated with the IMEI code can be queried in a database storing the historical data according to the IMEI code, that is: determining the last reported data which is most adjacent to the current reported data according to the time stamp of the data by the historical data of the moving body, subtracting the time stamps of the two reported data to obtain a time interval, judging whether the time interval is less than or equal to a preset interval, if so, indicating that the reported data is a travel point, and recording the reported data into a database; if not, the reported data is represented as an abnormal point, and the reported data is further analyzed by adopting the judging unit 13.
In an embodiment, the position trajectory-based slicing apparatus 1 shown in fig. 5 may further include: a processing unit 16.
And the processing unit 16 is configured to identify whether the reported data is data carrying position information.
If the reported data is data carrying position information, the identification unit 12 may be adopted to identify whether the reported data meets the preset condition according to the preset interval.
In this embodiment, in order to reduce the processing pressure of the massive reported data uploaded by the mobile body and improve the accuracy of data processing, data cleaning may be performed on the reported data, and data without location information may be filtered, for example: heartbeat data and alarm data (such as electric quantity alarm data, disassembly alarm data and the like).
A determining unit 13, configured to determine whether the reported data meets an adjustment interval condition when the location information of the reported data does not meet a preset condition.
It should be noted that: the adjustment interval condition is that the position information in the reported data is in a first preset range; the first preset range is at least one of a low signal area, a speed limit area and a congestion area.
In this embodiment, in consideration of the situation that a moving body may approach a low-signal, speed-limited or congested road segment during a driving process, so as to prolong a time interval of reported data, the reported data meeting the above situation is extracted by adjusting an interval condition, so that a preset interval is dynamically adjusted according to the situation of the road segment, and a situation that a path is erroneously split due to a fixed preset interval is prevented.
And an adjusting unit 14, configured to adjust the preset interval according to the reported data and historical data associated with the mobile body if the position information of the reported data meets the adjustment interval condition.
In an embodiment, the adjusting unit 14 is configured to, when the reported data meets the adjustment interval condition, obtain a standard driving time of an area corresponding to the position information in the reported data; and summing the standard running time and the preset interval, and taking the summed value as the preset interval.
In this embodiment, each first preset area corresponds to a standard travel time, the standard travel time is a fixed travel time passing through the first preset area, and a time interval obtained by summing the standard travel time and a preset interval is used as an adjusted preset interval, so as to achieve the purpose of prolonging the preset interval.
For example: the first preset area is a tunnel area, and the standard driving time T of the tunnel area1Tunnel length (tunnel group length)/lowest speed limit, adjusted preset interval T2＝T0+T1Wherein, T0At an initial preset interval.
In another embodiment, the adjusting unit 14 is configured to, when the reported data meets the adjustment interval condition, obtain a standard running time of a region corresponding to position information in the reported data and a current running average time of the region corresponding to the position information, sum the standard running time and the preset interval to obtain a sum value, obtain an average value of the sum value and the current running average time, and use the average value as the preset interval.
In the present embodiment, the current running average time is the average running time of all moving objects that approach the area corresponding to the position information (the first preset area) within a preset time period (for example, 1 hour or 2 hours). Adjusted preset interval T2＝(T0+T1+T3) /2, wherein, T3Is the current running mean time.
In another embodiment, the adjusting unit 14 is configured to, when the reported data meets the adjustment interval condition, obtain a standard running time of a region corresponding to position information in the reported data and a current running average time of the region corresponding to the position information, sum the standard running time and the preset interval to obtain a sum value, compare the sum value with the current running average, and take a larger value in a comparison result as the preset interval.
And a segmentation unit 15, configured to, if the position information of the reported data does not meet the adjustment interval condition, use the reported data as a starting segmentation point of a current trip, and use last reported data of the history data associated with the mobile body as an ending segmentation point of a last trip.
Further, the segmenting unit 15 is configured to, when the position information of the reported data does not meet the adjustment interval condition, identify whether the position information of the reported data is within a second preset area, if not, use the reported data as a starting segmentation point of a current trip, and use last reported data of the history data associated with the mobile body as an ending segmentation point of a last trip.
The second preset area is a preset highway section and/or a preset elevated road section.
Considering that when the position information of the reported data may be in a certain high-speed road section/elevated road section, it is obvious that the road section is not suitable for track segmentation, and the moving body may be in traffic jam or reporting equipment failure at present, the reported data can be regarded as a travel point and recorded in a database without performing subsequent process processing.
In the embodiment, the problem that the track is segmented into unreasonable areas can be solved. Such as the case of splitting a trajectory path at a highway segment. Of course, the coordinates of the high-speed service area and the like are not taken as abnormal points, so that the track segmentation of the service area is not influenced.
In this embodiment, the segmentation apparatus 1 based on the position trajectory determines whether the reported data belongs to a trip point or not by the way that the identification unit 12 identifies whether the reported data meets the preset condition according to the preset interval, and extracts an abnormal point; when the reported data belongs to the abnormal point, the judging unit 13 is adopted to judge whether the adjustment interval condition is met or not according to the position information, if so, the current moving body is in the area with weak signals (such as a tunnel road section, a congestion road section and the like), and the preset interval is prolonged through the adjusting unit 14, so that the condition that the stroke segmentation is wrong due to the fact that the reported data is delayed because the signal of the area where the moving body is located is weak is avoided, and the accuracy of the stroke segmentation is improved.
In an embodiment, the position trajectory-based slicing apparatus 1 shown in fig. 5 may further include: the unit 17 is initialized.
The initializing unit 17 is configured to initialize the preset interval, so that when the reported data of the mobile body is analyzed next time, the reported data is analyzed with reference to the original preset interval.
EXAMPLE III
In order to achieve the above object, the present invention further provides a computer device 2, where the computer device 2 includes a plurality of computer devices 2, components of the position-trajectory-based segmentation apparatus 1 according to the second embodiment may be dispersed in different computer devices 2, and the computer device 2 may be a smart phone, a tablet computer, a notebook computer, a desktop computer, a rack-mounted server, a blade server, a tower server, or a rack-mounted server (including an independent server, or a server cluster formed by a plurality of servers) that executes a program, or the like. The computer device 2 of the present embodiment includes at least, but is not limited to: a memory 21, a processor 23, a network interface 22 and a position trajectory based segmentation apparatus 1 (refer to fig. 6) which can be communicatively connected to each other through a system bus. It is noted that fig. 6 only shows the computer device 2 with components, but it is to be understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead.
In this embodiment, the memory 21 includes at least one type of computer-readable storage medium, which includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the storage 21 may be an internal storage unit of the computer device 2, such as a hard disk or a memory of the computer device 2. In other embodiments, the memory 21 may also be an external storage device of the computer device 2, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like provided on the computer device 2. Of course, the memory 21 may also comprise both an internal storage unit of the computer device 2 and an external storage device thereof. In this embodiment, the memory 21 is generally used to store an operating system installed in the computer device 2 and various application software, such as program codes of the location trajectory-based slicing method in the first embodiment. Further, the memory 21 may also be used to temporarily store various types of data that have been output or are to be output.
The processor 23 may be a Central Processing Unit (CPU), a controller, a microcontroller, a microprocessor, or other data Processing chip in some embodiments. The processor 23 is typically used for controlling the overall operation of the computer device 2, such as performing control and processing related to data interaction or communication with the computer device 2. In this embodiment, the processor 23 is configured to run the program code stored in the memory 21 or process data, for example, run the position-trajectory-based slicing apparatus 1.
The network interface 22 may comprise a wireless network interface or a wired network interface, and the network interface 22 is typically used to establish a communication connection between the computer device 2 and other computer devices 2. For example, the network interface 22 is used to connect the computer device 2 to an external terminal through a network, establish a data transmission channel and a communication connection between the computer device 2 and the external terminal, and the like. The network may be a wireless or wired network such as an Intranet (Intranet), the Internet (Internet), a Global System of Mobile communication (GSM), Wideband Code Division Multiple Access (WCDMA), a 4G network, a 5G network, Bluetooth (Bluetooth), Wi-Fi, and the like.
It is noted that fig. 6 only shows the computer device 2 with components 21-23, but it is to be understood that not all shown components are required to be implemented, and that more or less components may be implemented instead.
In this embodiment, the position-based trajectory segmentation apparatus 1 stored in the memory 21 may be further divided into one or more program modules, and the one or more program modules are stored in the memory 21 and executed by one or more processors (in this embodiment, the processor 23) to complete the present invention.
Example four
To achieve the above objects, the present invention also provides a computer-readable storage medium including a plurality of storage media such as a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application store, etc., on which a computer program is stored, which when executed by the processor 23, implements corresponding functions. The computer-readable storage medium of the present embodiment is used for storing the position-trajectory-based slicing apparatus 1, and when being executed by the processor 23, the computer-readable storage medium implements the position-trajectory-based slicing method of the first embodiment.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (10)

1. A segmentation method based on position tracks is characterized by comprising the following steps:
acquiring reported data of a mobile body;
identifying whether the reported data meets preset conditions or not according to preset intervals;
if the reported data does not accord with the preset condition, judging whether the position information of the reported data accords with the condition of an adjustment interval or not;
if the position information of the reported data accords with the adjustment interval condition, adjusting the preset interval according to the reported data and historical data associated with the moving body;
and if the position information of the reported data does not accord with the adjustment interval condition, taking the reported data as a starting dividing point of the current trip, and taking the last reported data of the historical data associated with the moving body as an ending dividing point of the last trip.
2. The method according to claim 1, wherein identifying whether the reported data meets a preset condition according to a preset interval further comprises:
and identifying whether the reported data is data carrying position information, if so, identifying whether the reported data meets a preset condition according to the preset interval.
3. The method according to claim 1, wherein identifying whether the reported data meets a preset condition according to a preset interval comprises:
calculating a time interval between the reported data and the last reported data according to the reported data and the historical data associated with the mobile body;
and judging whether the time interval meets a preset condition, wherein the preset condition is that the time interval is smaller than or equal to the preset interval.
4. The method according to claim 1, wherein the adjustment interval condition is that the position information in the reported data is in a first preset area;
the first preset area is at least one of a low signal area, a speed limit area and a congestion area.
5. The method according to claim 1, wherein if the position information of the reported data meets the adjustment interval condition, adjusting the preset interval according to the reported data and historical data associated with the mobile body comprises:
if the reported data accords with the adjustment interval condition, acquiring standard driving time of an area corresponding to the position information in the reported data;
and summing the standard running time and the preset interval, and taking the summed value as the preset interval.
6. The method according to claim 1, wherein if the position information of the reported data meets the adjustment interval condition, adjusting the preset interval according to the reported data and historical data associated with the mobile body comprises:
if the reported data accords with the adjustment interval condition, acquiring standard running time of a region corresponding to the position information in the reported data and current running average time of the region corresponding to the position information;
summing the standard running time and the preset interval to obtain a sum value;
acquiring the average value of the summation value and the current running average time, and taking the average value as the preset interval; or comparing the summation value with the current running average value, and taking the larger value in the comparison result as the preset interval.
7. The method according to claim 1, wherein if the position information of the reported data does not meet the adjustment interval condition, using the reported data as a starting point of a current trip and using a last reported data of the history data associated with the mobile body as an ending point of a last trip comprises:
if the position information of the reported data does not accord with the adjustment interval condition, identifying whether the position information of the reported data is in a second preset area or not;
and if not, taking the reported data as a starting cut point of the current trip, and taking the last reported data of the historical data associated with the moving body as an ending cut point of the last trip.
8. A segmentation device based on position track, characterized by comprising:
the mobile terminal comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring the reported data of the mobile body;
the identification unit is used for identifying whether the reported data meets a preset condition or not according to a preset interval;
the judging unit is used for judging whether the position information of the reported data accords with the regulation interval condition or not when the reported data does not accord with the preset condition;
the adjusting unit is used for adjusting the preset interval according to the reported data and historical data associated with the moving body when the position information of the reported data meets the adjustment interval condition;
and the segmentation unit is used for taking the reported data as a starting segmentation point of a current trip and taking the last reported data of the historical data associated with the moving body as an ending segmentation point of a last trip when the position information of the reported data does not accord with the adjustment interval condition.
9. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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